Simple Model: Weaknesses

In addition to all good things about the Simple Model of Spiking Neurons, there are few weaknesses that I observed while working on implementation of the model:

  • The model appears to be sensitive to large currents (for some types of neurons); there is probably something wrong with my implementation of the model
  • The model requires very small time step for some types of neurons (0.05ms for low threshold spiking (LTS) and fast spiking (FS) neurons); there is probably not much that can be done about this, but it definitely creates challenges with simulating those types of neurons as it requires to implement various time steps for different types of neurons to speed up the simulation. What's interesting, the previous version of the model had the same time step (0.5ms) for all types of neurons.

update 2005/12/21: According to the author of the model (private email):

There is indeed a problem with stability when the injected current, I, into a thalamic cell is too strong. You may interpret this as an artifact of the model, or as the sign that such a strong current can kill the cell.

update 2005/12/22: Again, from the author of the model on the same problem (private email):

What happens is that the peak of the spike and the voltage reset variables meet, periodic spiking disappears, and the voltage variable drops. This behavior resembles the "excitation block" behavior of many neurons (no spiking when the input current is too strong), though the mechanism may be different.

update 2005/12/22: And here is the recomendation on how to fix this:

...use the following formula for the after-spike reseting of u: u <- min(u+50,530). This way, u never goes above 530, and the the spike cutoff and afterspike reset values never intersect.

Here is the result and the modified script.

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